National soil organic carbon map of agricultural lands in Nepal

Abstract

Reliable and accurate soil organic carbon (SOC) maps are needed to monitor and improve SOC status in croplands and for agro-environmental applications. Topsoil (0–20 cm) SOC content from agricultural lands was predicted and mapped with quantified uncertainty across Nepal using state-of-the-art soil mapping techniques. Altogether 25,312 SOC observations were used to build and evaluate prediction models derived from four machine learning algorithms, namely Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB) and Support Vector Machines. Twenty two environmental variables were selected as SOC predictors based on their correlation with measured SOC contents and non-collinearity with other predictors. The predictive performance of these models was compared using calibration (80% observations) and validation (20% observations) datasets. The performance of the models was also compared against a global SOC dataset compiled by International Soil Reference and Information Centre (ISRIC). The best model among the four algorithms was used to map and quantify the spatial distribution of SOC contents, and the model uncertainty was assessed with the Quantile Regression Forests technique with standard deviation representing prediction uncertainty. The RF model performed the best among all tested models, closely followed by the Cubist, and then the XGB model. The predictive performance of all of these models was better than the global SOC prediction from ISRIC. This study provides a baseline map for the topsoil SOC contents from the croplands in Nepal, and also provides a reference for similar SOC mapping studies.